The present invention relates generally to tomosynthesis imaging systems. More particularly, the invention relates to a technique for correcting or avoiding certain artifacts and generally improving tomosynthesis images by use of non-uniform view weighting.
Tomosynthesis systems have developed over the past decades and now offer significant advantages for many types of imaging, such as in the medical diagnostics field. In general, X-ray tomosynthesis involves the use of low dose X-ray radiation to produce a series of images acquired over a range of X-ray beam orientations relative to an image object. In currently available systems the object is positioned in front of a digital detector and the X-ray source is moved to various positions to produce the series of images. A number of such images may be produced, typically in excess of 50 or 60. The detector collects electrical data representative of the depletion of a charge at individual pixel locations resulting from continuation of the X-ray radiation at those locations by intervening objects, such as the features of the images subject, a patient in the medical diagnostics context, for example. Acquiring images of the subject from a different orientations of the X-ray beam allows depth information to be incorporated into the final 3D image. The depth information is unavailable in conventional projection X-ray imaging, making tomosynthesis attractive for identifying specific features of interest and their general location within the subject.
Amorphous silicon flat panel digital X-ray detectors are currently available for tomosynthesis imaging. In general, however, any X-ray detector that provides a digital projection image may be used. These may include, for example, charge coupled device (CCD) arrays, digitized film screens, or other digital detectors, such as direct conversion detectors. The low electronic noise and fast read-out times of such detectors enable acquisitions with many projections at low overall patient dose as compared with competing detector technologies.
Following acquisition of the image data, tomosynthesis techniques include reconstruction of images at various “slices” through the subject. Reconstruction algorithms permit reconstruction of many such slices at different spatial planes, typically parallel to the imaging plane of the detector. Such slices contain different anatomies located at various heights above the detector with underlying and overlying structures being generally suppressed. The generation of slices from projection images typically contains a chain of processing and operations. It should be noted that the term “projection” generally refers to a specific geometry or positioning of the X-ray source with respect to the subject and detector, many such projections being used in generating the slices as noted above.
Processing and operations used to generate slices from projection images in tomosynthesis include, but are not limited to, standard pre-processing steps, special pre-processing steps, reconstruction steps, and post-processing operations. Standard pre-processing operations include detector corrections, such as for gain, offset, bad pixels in the detector, and so forth. These may also include correction for geometry distortions, log transformation to store a “film-like look” and so forth. Special pre-processing steps may include bad detector edge correction, padding, beam hardening correction, off-focal radiation correction, reference normalization, and so forth. Reconstruction steps and algorithms may also vary. Many such algorithms are based on filtered back-projection principles, such as shift and add techniques, generalized filtered back-projection techniques, order statistics back-projections, and so forth. Algorithms based on back-projection filtering principles in which back-projection is performed first followed by 2D/3D filtering, and algorithms based on minimum-norm solutions are also available. These include algorithms known in the field as ART, DART, MITS, TACT, Fourier-based reconstruction, objective function-base reconstruction, ML, MAP, and so forth, and combinations of these. Post-processing may include various types of image and contrast enhancement, such as tissue equalization, thickness compensation, brightness and white balancing, and additional artifact management routines.
In general, back-projection is the favored process to generate tomosynthesis slices, and is typically used in many of the reconstruction algorithms listed above. In back-projection, pixel intensity values are assigned to computed pixels of slices at the various levels above the imaging plane to form the slice images from the projection data. This is generally done by assigning values of pixels at each slice by dividing the intensity of the pixels in the projection data by the number of slices to be reconstructed. This uniform view weighting, however, causes many problems and inconsistencies in the reconstructed images. These issues may, then, result in artifacts such as wavy patterns in the reconstructed images, general fall off of intensity values near edges of the images, and ghost-like images at elevations above, image plane where the subject cannot have been located (i.e. beyond the physical limits of the subject being imaged). There is a need, therefore, for improved techniques for tomosynthesis image data processing. There is a particular need for a technique which accounts for a wide range of factors which can render reconstructed images inconsistent or otherwise degrade the images. Needs exist, for example, for techniques that reduce the fall-off of data near the edges of slice images, that reduce the assignment of values beyond the limits of the subject, and that reduce the deformity or inconsistency of the resulting data that can result from many factors affecting the underlying projection data.